WO2024120761A1 - Event detection method - Google Patents
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- WO2024120761A1 WO2024120761A1 PCT/EP2023/081675 EP2023081675W WO2024120761A1 WO 2024120761 A1 WO2024120761 A1 WO 2024120761A1 EP 2023081675 W EP2023081675 W EP 2023081675W WO 2024120761 A1 WO2024120761 A1 WO 2024120761A1
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- 238000001514 detection method Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 30
- 230000007613 environmental effect Effects 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 20
- 239000002689 soil Substances 0.000 claims description 8
- 238000013473 artificial intelligence Methods 0.000 claims description 6
- 239000003673 groundwater Substances 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000011144 upstream manufacturing Methods 0.000 claims description 4
- IOVCWXUNBOPUCH-UHFFFAOYSA-M Nitrite anion Chemical compound [O-]N=O IOVCWXUNBOPUCH-UHFFFAOYSA-M 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 150000002500 ions Chemical class 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 5
- 238000012360 testing method Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 239000010865 sewage Substances 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
Definitions
- the invention relates to a method for detecting an environmental event according to the preamble of claim 1 and a system for carrying out the method.
- Early warning systems often include sensors, event detection and decision subsystems to identify hazards early. Historical data and predictive models are often used to detect such an event. For example, to predict a flood event, rainfall data is continuously fed into a model. The simulation results are then displayed as discharge and water level forecasts at predefined desired locations.
- these event detection methods are based on a single measured variable. Although some methods are based on more than one variable, they often do not provide the relationship between the variables. Therefore, the capabilities in terms of warning and prediction/forecasting are limited.
- the object of the present invention is to provide a more detailed and precise prediction method for environmental events.
- the task is solved with the following steps: a) measuring at least two variables that are detected by at least one detection system arranged at a decentralized measuring point; b) transmitting the measured values of the at least two variables from the detection system to a data processing unit which is designed to execute a model for evaluating the current environmental state and/or for predicting the possibility of an environmental event occurring.
- the method is characterized in that the correlation of the at least two variables is measured, which is based on cross-correlation.
- Cross-correlation is a measure of the similarity of two data series depending on the shift relative to each other, which is often used in signal processing.
- the at least two variables are measured continuously in a predefined measurement interval.
- the detection system comprises at least one of: a sensor, a measuring device, a geographic information system (GIS) model, a web service, and/or a database.
- GIS geographic information system
- the data processing unit is part of the recognition system.
- the data processing unit is located in a central station for remote monitoring to be protected from damage during the environmental event.
- the model comprises a simulation based on artificial intelligence techniques.
- the model is trained based on historical measurements of the at least two variables.
- the at least two variables comprise at least two of a soil moisture, a water level, a pressure head/groundwater pressure head (or pressure), a water temperature, a pH, a conductivity, a turbidity, a flow, an oxygen content, a nitrite content, a soil bearing capacity and/or other ion concentration data, geological data, geographical data, climate data, weather data, weather forecast data.
- the environmental event includes at least one of a flood, a landslide, an unauthorized discharge/withdrawal of water, and/or an upstream chemical spill.
- a system comprising at least one detection system arranged at a decentralized measuring point; and a data processing unit designed to execute a model for evaluating the current environmental state and/or for predicting the possibility of an environmental event occurring in order to carry out the method described above.
- FIG 1 shows an embodiment of the claimed measuring system.
- identical features are marked with the same reference numerals.
- a detection system 1 e.g. a field device
- a detection system 1 comprises at least one sensor arranged at a measuring point near a river 2 (could also be other water bodies, such as a lake, a reservoir) or on a slope 3 (e.g. for detecting a landslide event), wherein the detection system 1 measures two variables (m, n): soil moisture and water level.
- the variables could also be at least one of a pressure head/groundwater head (or pressure), a water temperature, a flow, a soil bearing capacity and/or other geological data, geographical data, climate data, weather data, weather forecast data (differentiation of rain/snow).
- the two variables (m, n) are then continuously measured in a predefined measuring interval.
- the measured values (mt, nt) of the two variables are then transmitted to a data processing unit 4 which is designed to execute a model for evaluating the current risk of flooding and/or for predicting the possibility of a flooding event occurring.
- the data processing unit 4 is located in a central station for remote monitoring, which is connected to the detection system 1 by wire or wirelessly.
- the model includes AI (artificial intelligence) based methods (e.g. anomaly detection) which are trained based on the historical measurements for the two variables (m, n).
- AI artificial intelligence
- the model is improved using cross-correlation. Specifically, it is used to test whether the two variables (m, n) are correlated and, if so, to test the time lag between the two sets of variables (m, n). High soil moisture can indicate a high probability of flood or landslide events. When abnormal water levels are detected, it is often a sign of a flood or landslide.
- the correlation and time lag between the two sets of variables (m, n) could improve the detection method by providing a more detailed and accurate prediction.
- a further implementation of the invention is a method for detecting an unauthorized discharge/withdrawal of water and/or predicting what has happened upstream.
- a detection system 1 comprises two sensors arranged at a measuring point in a river that measures two variables (m, n): water level and water temperature.
- variables m, n
- other variables could also be used for the method, such as soil moisture, head/groundwater head (or pressure), flow, water temperature, pH, conductivity, turbidity, oxygen content, nitrite content and/or other ion concentration data, climate data, weather data, weather forecast data (differentiation of rain/snow).
- the two variables (m, n) are then continuously measured at a predefined measuring interval.
- the measured values (mt, nt) of the two variables are then transmitted to a data processing unit 4 which is designed to execute a model for evaluating the current water condition, for detecting an unauthorized discharge/withdrawal of water and/or for predicting the possibility of an upstream chemical accident.
- the data processing unit 4 could be a computer in a central station for remote monitoring.
- the model includes AI (artificial intelligence) based procedures (e.g. anomaly detection) that are trained based on the historical measurements of the two variables (m, n). An abnormal water level or temperature may indicate an unauthorized discharge/withdrawal of water.
- the model is then trained using of cross-correlation. In particular, it is used to test whether the two variables (m, n) are correlated and, if so, to test the time lag between the two sets of variables (m, n).
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Abstract
The invention discloses a detection method and a system for carrying out the method. The method comprises the followings steps: a) measuring at least two variables which are detected by at least one detection system that is arranged at a decentralised measuring point; b) transferring the measurement values of the at least two variables from the detection system to a data processing unit which is designed to execute a model for evaluating the current environmental condition and/or for predicting the possibility of an environmental event occurring. The method is characterised in that the correlation of the at least two variables, which is based on cross-correlation, is measured.
Description
Ereigniserkennungsverfahren Event detection procedure
Die Erfindung betrifft ein Verfahren zum Erkennen eines Umweltereignisses gemäß dem Oberbegriff nach Anspruch 1 und ein System zum Durchführen des Verfahrens. The invention relates to a method for detecting an environmental event according to the preamble of claim 1 and a system for carrying out the method.
Frühwarnsysteme stellen Informationen über Bereiche bereit, die anfällig für spezifische Umweltereignisse wie etwa Naturkatastrophen sind, beispielsweise ein Erdrutsch, eine Überflutung und ein Erdbeben, eine nicht autorisierte Einleitung ungeklärter oder unzureichend behandelter Abwässer in einer Zone, in der keine Einleitung erlaubt ist, und eine illegale Entnahme von Wasser. Diese Umweltereignisse können lebensbedrohlich oder umweltschädlich sein, sodass eine genaue Vorhersage/Prognose und Warnung wichtig sind. Early warning systems provide information on areas vulnerable to specific environmental events such as natural disasters such as a landslide, flood and earthquake, unauthorized discharge of raw or inadequately treated sewage into a zone where discharge is not permitted, and illegal abstraction of water. These environmental events can be life-threatening or environmentally harmful, so accurate forecasting and warning are important.
Frühwarnsysteme umfassen häufig Messaufnehmer, Ereigniserkennungs- und Entscheidungssubsysteme zum frühzeitigen Identifizieren von Gefahren. Historische Daten und Vorhersagemodelle werden häufig verwendet, um ein solches Ereignis zu erkennen. Zum Beispiel werden zum Vorhersagen eines Überflutungsereignisses Niederschlagsdaten kontinuierlich in ein Modell eingespeist. Die Simulationsergebnisse werden dann als Abfluss- und Wasserstandsvorhersagen an vordefinierten gewünschten Orten angezeigt. Early warning systems often include sensors, event detection and decision subsystems to identify hazards early. Historical data and predictive models are often used to detect such an event. For example, to predict a flood event, rainfall data is continuously fed into a model. The simulation results are then displayed as discharge and water level forecasts at predefined desired locations.
Üblicherweise basieren diese Ereigniserkennungsverfahren auf einer einzigen gemessenen GrößeA/ariablen. Obwohl einige Verfahren auf mehr als einer Variablen basieren, stellen sie oft nicht die Beziehung zwischen den Variablen bereit. Daher sind die Fähigkeiten in Bezug auf die Warnung und die Vorhersage/Prognose beschränkt. Typically, these event detection methods are based on a single measured variable. Although some methods are based on more than one variable, they often do not provide the relationship between the variables. Therefore, the capabilities in terms of warning and prediction/forecasting are limited.
Die Aufgabe der vorliegenden Erfindung besteht darin, ein detaillierteres und präziseres Vorhersage-ZPrognoseverfahren für Umweltereignisse bereitzustellen. The object of the present invention is to provide a more detailed and precise prediction method for environmental events.
Die Aufgabe wird durch das in dem unabhängigen Anspruch 1 angegebene Verfahren und das in Anspruch 10 beschriebene System zum Durchführen des Verfahrens erfüllt. The object is achieved by the method specified in independent claim 1 and the system for carrying out the method described in claim 10.
Hinsichtlich des Verfahrens wird die Aufgabe mit den folgenden Schritten gelöst: a) Messen mindestens zweier Variablen, die durch mindestens ein Erkennungssystem erkannt werden, das an einem dezentralen Messpunkt angeordnet ist; b) Übertragen
der Messwerte der mindestens zwei Variablen von dem Erkennungssystem an eine Datenverarbeitungseinheit, die dazu ausgebildet ist, ein Modell zum Auswerten des aktuellen Umweltzustands und/oder zum Vorhersagen der Möglichkeit des Eintretens eines Umweltereignisses auszuführen. Das Verfahren ist dadurch gekennzeichnet, dass die Korrelation der mindestens zwei Variablen gemessen wird, die auf Kreuzkorrelation basiert. Kreuzkorrelation ist ein Maß für die Ähnlichkeit von zwei Datenreihen in Abhängigkeit von der Verschiebung relativ zueinander, das in der Signalverarbeitung häufig verwendet wird. With regard to the method, the task is solved with the following steps: a) measuring at least two variables that are detected by at least one detection system arranged at a decentralized measuring point; b) transmitting the measured values of the at least two variables from the detection system to a data processing unit which is designed to execute a model for evaluating the current environmental state and/or for predicting the possibility of an environmental event occurring. The method is characterized in that the correlation of the at least two variables is measured, which is based on cross-correlation. Cross-correlation is a measure of the similarity of two data series depending on the shift relative to each other, which is often used in signal processing.
Vorteilhafterweise werden die mindestens zwei Variablen in einem vordefinierten Messintervall kontinuierlich gemessen. Advantageously, the at least two variables are measured continuously in a predefined measurement interval.
In einer Ausführungsform umfasst das Erkennungssystem mindestens eines von: einem Messaufnehmer, einer Messvorrichtung, einem Modell eines geografischen Informationssystems (GIS-Modell), einem Webdienst und/oder einer Datenbank. In one embodiment, the detection system comprises at least one of: a sensor, a measuring device, a geographic information system (GIS) model, a web service, and/or a database.
In einer Ausführungsform ist die Datenverarbeitungseinheit Teil des Erkennungssystems. In one embodiment, the data processing unit is part of the recognition system.
In einer Ausführungsform befindet sich die Datenverarbeitungseinheit in einer zentralen Station für Fernüberwachung, um vor Beschädigung bei dem Umweltereignis geschützt zu sein. In one embodiment, the data processing unit is located in a central station for remote monitoring to be protected from damage during the environmental event.
In einer Ausführungsform umfasst das Modell eine Simulation basierend auf Verfahren mit künstlicher Intelligenz. In one embodiment, the model comprises a simulation based on artificial intelligence techniques.
In einer Ausführungsform wird das Modell basierend auf historischen Messwerten der mindestens zwei Variablen trainiert. In one embodiment, the model is trained based on historical measurements of the at least two variables.
In einer Ausführungsform umfassen die mindestens zwei Variablen mindestens zwei von einer Bodenfeuchtigkeit, einem Wasserstand, einer Druckhöhe/Grundwasserdruckhöhe (oder einem Druck), einer Wassertemperatur, einem pH-Wert, einer Leitfähigkeit, einer Trübung, einem Durchfluss, einem Sauerstoffgehalt, einem Nitritgehalt, einer Bodentragfähigkeit und/oder sonstigen lonenkonzentrationsdaten, geologischen Daten, geografischen Daten, Klimadaten, Wetterdaten, Wetterprognosedaten.
In einer Ausführungsform umfasst das Umweltereignis mindestens eines von einer Überflutung, einem Erdrutsch, einer nicht autorisierten Einleitung/Entnahme von Wasser und/oder einem ström aufwärtigen Chemieunfall. In one embodiment, the at least two variables comprise at least two of a soil moisture, a water level, a pressure head/groundwater pressure head (or pressure), a water temperature, a pH, a conductivity, a turbidity, a flow, an oxygen content, a nitrite content, a soil bearing capacity and/or other ion concentration data, geological data, geographical data, climate data, weather data, weather forecast data. In one embodiment, the environmental event includes at least one of a flood, a landslide, an unauthorized discharge/withdrawal of water, and/or an upstream chemical spill.
Diese Aufgabe wird ferner durch ein System erzielt, umfassend mindestens ein Erkennungssystem, das an einem dezentralen Messpunkt angeordnet ist; und eine Datenverarbeitungseinheit, die dazu ausgebildet ist, ein Modell zum Auswerten des aktuellen Umweltzustands und/oder zum Vorhersagen der Möglichkeit des Eintretens eines Umweltereignisses auszuführen, um das vorstehend beschriebene Verfahren durchzuführen. This object is further achieved by a system comprising at least one detection system arranged at a decentralized measuring point; and a data processing unit designed to execute a model for evaluating the current environmental state and/or for predicting the possibility of an environmental event occurring in order to carry out the method described above.
Dies wird unter Bezugnahme auf die folgende Figur näher erläutert. This is explained in more detail with reference to the following figure.
Die Figur 1 zeigt eine Ausführungsform des beanspruchten Messsystems. In der Figur 1 sind gleiche Merkmale mit denselben Bezugszeichen markiert. Figure 1 shows an embodiment of the claimed measuring system. In Figure 1, identical features are marked with the same reference numerals.
Unter Bezugnahme auf die Figur 1 wird das Verfahren zum Erkennen/Vorhersagen eines Überflutungsereignisses, eines Erdrutschereignisses oder anderer Naturkatastrophen verwendet. Ein Erkennungssystem 1 (z. B. ein Feldgerät) umfasst mindestens einen Messaufnehmer, der an einem Messpunkt nahe einem Fluss 2 (könnten auch andere Wasserkörper sein, wie ein See, ein Stausee) oder an einem Hang 3 (z. B. zum Erkennen eines Erdrutschereignisses) angeordnet ist, wobei das Erkennungssystem 1 zwei Variablen (m, n) misst: Bodenfeuchtigkeit und Wasserstand. Die Variablen könnten jedoch auch mindestens eines von einer Druckhöhe/Grundwasserdruckhöhe (oder einem Druck), einer Wassertemperatur, einem Durchfluss, einer Bodentragfähigkeit und/oder sonstigen geologischen Daten, geografischen Daten, Klimadaten, Wetterdaten, Wetterprognosedaten (Unterscheidung von Regen/Schnee) sein. Die zwei Variablen (m, n) werden dann in einem vordefinierten Messintervall kontinuierlich gemessen. Die Messwerte (mt, nt) der zwei Variablen werden dann an eine Datenverarbeitungseinheit 4 übertragen, die dazu ausgebildet ist, ein Modell zum Auswerten des aktuellen Risikos von Überflutung und/oder zum Vorhersagen der Möglichkeit des Eintretens eines Überflutungsereignisses auszuführen. With reference to Figure 1, the method is used for detecting/predicting a flood event, a landslide event or other natural disasters. A detection system 1 (e.g. a field device) comprises at least one sensor arranged at a measuring point near a river 2 (could also be other water bodies, such as a lake, a reservoir) or on a slope 3 (e.g. for detecting a landslide event), wherein the detection system 1 measures two variables (m, n): soil moisture and water level. However, the variables could also be at least one of a pressure head/groundwater head (or pressure), a water temperature, a flow, a soil bearing capacity and/or other geological data, geographical data, climate data, weather data, weather forecast data (differentiation of rain/snow). The two variables (m, n) are then continuously measured in a predefined measuring interval. The measured values (mt, nt) of the two variables are then transmitted to a data processing unit 4 which is designed to execute a model for evaluating the current risk of flooding and/or for predicting the possibility of a flooding event occurring.
Die Datenverarbeitungseinheit 4 befindet sich in einer zentralen Station für Fernüberwachung, die mit dem Erkennungssystem 1 mit Draht oder drahtlos verbunden
ist. Das Modell umfasst auf Kl (künstlicher Intelligenz) basierende Verfahren (z. B. Anomalieerfassung), die basierend auf den historischen Messwerten für die zwei Variablen (m, n) trainiert werden. Das Modell wird unter Verwendung von Kreuzkorrelation verbessert. Insbesondere wird es verwendet, um zu testen, ob die zwei Variablen (m, n) korrelieren und, falls ja, die Zeitverzögerung zwischen den zwei Reihen der Variablen (m, n) zu testen. Hohe Bodenfeuchtigkeit kann auf eine hohe Wahrscheinlichkeit von Überflutungs- oder Erdrutschereignissen hinweisen. Wenn abnormale Wasserstände erkannt werden, ist dies oft ein Anzeichen für eine Überflutung oder einen Erdrutsch. Die Korrelation und die Zeitverzögerung zwischen den zwei Reihen der Variablen (m, n) könnten das Erkennungsverfahren durch eine detailliertere und genauere Vorhersage verbessern. The data processing unit 4 is located in a central station for remote monitoring, which is connected to the detection system 1 by wire or wirelessly. is. The model includes AI (artificial intelligence) based methods (e.g. anomaly detection) which are trained based on the historical measurements for the two variables (m, n). The model is improved using cross-correlation. Specifically, it is used to test whether the two variables (m, n) are correlated and, if so, to test the time lag between the two sets of variables (m, n). High soil moisture can indicate a high probability of flood or landslide events. When abnormal water levels are detected, it is often a sign of a flood or landslide. The correlation and time lag between the two sets of variables (m, n) could improve the detection method by providing a more detailed and accurate prediction.
Eine weitere Implementierung der Erfindung ist ein Verfahren zum Erkennen einer nicht autorisierten Einleitung/Entnahme von Wasser und/oder zum Vorhersagen dessen, was stromaufwärts geschehen ist. Ein Erkennungssystem 1 umfasst zwei Messaufnehmer, die an einem Messpunkt in einem Fluss angeordnet sind, der zwei Variablen (m, n) misst: Wasserstand und Wassertemperatur. Für das Verfahren könnten jedoch auch andere Variablen verwendet werden, wie eine Bodenfeuchtigkeit, eine Druckhöhe/Grundwasserdruckhöhe (oder ein Druck), ein Durchfluss, eine Wassertemperatur, ein pH-Wert, eine Leitfähigkeit, eine Trübung, ein Sauerstoffgehalt, ein Nitritgehalt und/oder sonstige lonenkonzentrationsdaten, Klimadaten, Wetterdaten, Wetterprognosedaten (Unterscheidung von Regen/Schnee). Die zwei Variablen (m, n) werden dann n einem vordefinierten Messintervall kontinuierlich gemessen. Die Messwerte (mt, nt) der zwei Variablen werden dann an eine Datenverarbeitungseinheit 4 übertragen, die dazu ausgebildet ist, ein Modell zum Auswerten des aktuellen Wasserzustands, zum Erkennen einer nicht autorisierten Einleitung/Entnahme von Wasser und/oder zum Vorhersagen der Möglichkeit eines ström aufwärtigen Chemieunfalls auszuführen. A further implementation of the invention is a method for detecting an unauthorized discharge/withdrawal of water and/or predicting what has happened upstream. A detection system 1 comprises two sensors arranged at a measuring point in a river that measures two variables (m, n): water level and water temperature. However, other variables could also be used for the method, such as soil moisture, head/groundwater head (or pressure), flow, water temperature, pH, conductivity, turbidity, oxygen content, nitrite content and/or other ion concentration data, climate data, weather data, weather forecast data (differentiation of rain/snow). The two variables (m, n) are then continuously measured at a predefined measuring interval. The measured values (mt, nt) of the two variables are then transmitted to a data processing unit 4 which is designed to execute a model for evaluating the current water condition, for detecting an unauthorized discharge/withdrawal of water and/or for predicting the possibility of an upstream chemical accident.
Die Datenverarbeitungseinheit 4 könnte ein Computer in einer zentralen Station für Fernüberwachung sein. Das Modell umfasst auf Kl (künstlicher Intelligenz) basierende Verfahren (z. B. Anomalieerfassung), die basierend auf den historischen Messwerten der zwei Variablen (m, n) trainiert werden. Ein abnormaler Wasserstand oder eine abnormale Wassertemperatur können auf eine nicht autorisierte Einleitung/Entnahme von Wasser hinweisen. Das Modell wird dann unter Verwendung
von Kreuzkorrelation verbessert. Insbesondere wird es verwendet, um zu testen, ob die zwei Variablen (m, n) korrelieren und, falls ja, die Zeitverzögerung zwischen den zwei Reihen von Variablen (m, n) zu testen.
The data processing unit 4 could be a computer in a central station for remote monitoring. The model includes AI (artificial intelligence) based procedures (e.g. anomaly detection) that are trained based on the historical measurements of the two variables (m, n). An abnormal water level or temperature may indicate an unauthorized discharge/withdrawal of water. The model is then trained using of cross-correlation. In particular, it is used to test whether the two variables (m, n) are correlated and, if so, to test the time lag between the two sets of variables (m, n).
Bezugszeichenliste Erkennungssystem Fluss Hang Datenverarbeitungseinheit
List of reference symbols Detection system River slope Data processing unit
Claims
1 . Verfahren zum Erkennen eines Umweltereignisses, umfassend die Schritte: a) Messen mindestens zweier Variablen (m, n), die durch mindestens ein Erkennungssystem (1 ) erkannt werden, das an einem dezentralen Messpunkt angeordnet ist; b) Übertragen der Messwerte (mt, nt) der mindestens zwei Variablen (m, n) von dem Erkennungssystem (1 ) an eine Datenverarbeitungseinheit (4), die ausgebildet ist, um ein Modell auszuführen, um den aktuellen Umweltzustand auszuwerten und/oder um die Möglichkeit des Eintretens eines Umweltereignisses vorherzusagen; dadurch gekennzeichnet, dass die Korrelation der mindestens zwei Variablen (m, n) gemessen wird, die auf Kreuzkorrelation basiert. 1 . Method for detecting an environmental event, comprising the steps of: a) measuring at least two variables (m, n) detected by at least one detection system (1) arranged at a decentralized measuring point; b) transmitting the measured values (mt, nt) of the at least two variables (m, n) from the detection system (1) to a data processing unit (4) designed to execute a model to evaluate the current environmental state and/or to predict the possibility of an environmental event occurring; characterized in that the correlation of the at least two variables (m, n) is measured, which is based on cross-correlation.
2. Verfahren nach Anspruch 1 , dadurch gekennzeichnet, dass die mindestens zwei Variablen (m, n) in einem vordefinierten Messintervall kontinuierlich gemessen werden. 2. Method according to claim 1, characterized in that the at least two variables (m, n) are measured continuously in a predefined measuring interval.
3. Verfahren nach mindestens einem der Ansprüche 1 bis 2, dadurch gekennzeichnet, dass das Erkennungssystem (1 ) mindestens eines umfasst von: einem Messaufnehmer, einer Messvorrichtung, einem Modell eines geografischen Informationssystems (GIS-Modell), einem Webdienst und/oder einer Datenbank. 3. Method according to at least one of claims 1 to 2, characterized in that the recognition system (1) comprises at least one of: a measuring sensor, a measuring device, a model of a geographic information system (GIS model), a web service and/or a database.
4. Verfahren nach mindestens einem der Ansprüche 1 bis 3, dadurch gekennzeichnet, dass die Datenverarbeitungseinheit (4) Teil des Erkennungssystems (1) ist. 4. Method according to at least one of claims 1 to 3, characterized in that the data processing unit (4) is part of the recognition system (1).
5. Verfahren nach mindestens einem der Ansprüche 1 bis 3, dadurch gekennzeichnet, dass sich die Datenverarbeitungseinheit (4) in einer zentralen Station für Fernüberwachung befindet. 5. Method according to at least one of claims 1 to 3, characterized in that the data processing unit (4) is located in a central station for remote monitoring.
6. Verfahren nach mindestens einem der Ansprüche 1 bis 5, dadurch gekennzeichnet, dass das Modell eine Simulation basierend auf Verfahren mit künstlicher Intelligenz umfasst.
6. Method according to at least one of claims 1 to 5, characterized in that the model comprises a simulation based on artificial intelligence methods.
7. Verfahren nach mindestens einem der Ansprüche 1 bis 6, dadurch gekennzeichnet, dass das Modell basierend auf historischen Messwerten der mindestens zwei Variablen (m, n) trainiert wird. 7. Method according to at least one of claims 1 to 6, characterized in that the model is trained based on historical measured values of the at least two variables (m, n).
8. Verfahren nach mindestens einem der Ansprüche 1 bis 7, dadurch gekennzeichnet, dass die mindestens zwei Variablen (m, n) mindestens zwei von einer Bodenfeuchtigkeit, einem Wasserstand, einer Druckhöhe/Grundwasserdruckhöhe (oder einem Druck), einer Wassertemperatur, einem pH-Wert, einer Leitfähigkeit, einer Trübung, einem Durchfluss, einem Sauerstoffgehalt, einem Nitritgehalt, einer Bodentragfähigkeit und/oder sonstigen lonenkonzentrationsdaten, geologischen Daten, geografischen Daten, Klimadaten, Wetterdaten, Wetterprognosedaten umfassen. 8. Method according to at least one of claims 1 to 7, characterized in that the at least two variables (m, n) comprise at least two of a soil moisture, a water level, a pressure head/groundwater pressure head (or a pressure), a water temperature, a pH value, a conductivity, a turbidity, a flow, an oxygen content, a nitrite content, a soil bearing capacity and/or other ion concentration data, geological data, geographical data, climate data, weather data, weather forecast data.
9. Verfahren nach mindestens einem der Ansprüche 1 bis 8, dadurch gekennzeichnet, dass das Umweltereignis mindestens eines von einer Überflutung, einem Erdrutsch, einer nicht autorisierten Einleitung/Entnahme von Wasser und/oder einem ström aufwärtigen Chemieunfall umfasst. 9. Method according to at least one of claims 1 to 8, characterized in that the environmental event comprises at least one of a flood, a landslide, an unauthorized discharge/withdrawal of water and/or an upstream chemical accident.
10. System zum Implementieren des Verfahrens nach mindestens einem der Ansprüche 1 bis 9, umfassend mindestens ein Erkennungssystem (1 ), das an einem dezentralen Messpunkt angeordnet ist; und eine Datenverarbeitungseinheit (4), die ausgebildet ist, um ein Modell auszuführen, um den aktuellen Umweltzustand auszuwerten und/oder die Möglichkeit des Eintretens eines Umweltereignisses vorherzusagen.
10. System for implementing the method according to at least one of claims 1 to 9, comprising at least one detection system (1) arranged at a decentralized measuring point; and a data processing unit (4) designed to execute a model to evaluate the current environmental state and/or to predict the possibility of an environmental event occurring.
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| DE102022132383.8A DE102022132383A1 (en) | 2022-12-06 | 2022-12-06 | Event detection procedure |
| DE102022132383.8 | 2022-12-06 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170193305A1 (en) * | 2014-06-16 | 2017-07-06 | Agt International Gmbh | Flash flooding detection system |
| CN111275931A (en) * | 2019-12-24 | 2020-06-12 | 湖北民族大学 | Dangerous rock fracture early warning method and system |
| CN112699572A (en) * | 2021-01-18 | 2021-04-23 | 长安大学 | Method for predicting landslide deformation based on time-lag correlation analysis |
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| DE102009020709A1 (en) | 2009-05-11 | 2010-11-18 | Basso, Gertrud | Method for monitoring and analyzing territory and states of air and vegetation in forest land, involves storing information and operator decisions in respective database for analysis calculation of images |
| DE102016219029A1 (en) | 2016-09-30 | 2018-04-05 | Ford Global Technologies, Llc | Method and device for generating information about the salinity of a roadway and use of the information |
| DE102019203895A1 (en) | 2019-03-21 | 2020-09-24 | Robert Bosch Gmbh | Method for evaluating at least one signal |
| DE102020134382A1 (en) | 2020-12-21 | 2022-06-23 | Endress+Hauser SE+Co. KG | Device for measuring precipitation |
| DE202022100778U1 (en) | 2022-02-10 | 2022-03-01 | Vinayak Ashok Bharadi | IoT-based automated system for air quality prediction using an unmanned aerial vehicle (UAV) |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20170193305A1 (en) * | 2014-06-16 | 2017-07-06 | Agt International Gmbh | Flash flooding detection system |
| CN111275931A (en) * | 2019-12-24 | 2020-06-12 | 湖北民族大学 | Dangerous rock fracture early warning method and system |
| CN112699572A (en) * | 2021-01-18 | 2021-04-23 | 长安大学 | Method for predicting landslide deformation based on time-lag correlation analysis |
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